Description:Field of Invention
An Intelligent Traffic Management Systems, specifically to a Smart Urban Traffic System that utilizes real-time vehicle tracking for enhancing traffic flow and safety. It uses advanced object detection and tracking technologies, including YOLOv11 and BotSort, to accurately identify and follow vehicles in urban environments. It incorporates emergency vehicle detection to prioritize critical situations, facilitating swift responses. The system utilizes TransferFlow for efficient data management and model optimization. It also leverages Deep Q-learning, a reinforcement learning approach, to optimize traffic control decisions in response todynamic conditions, aiming to reduce congestion and improve traffic efficiency.

Background of the Invention
Urbanization is accompanied by a growing number of problems such as traffic congestion, road safety, and the efficient management of emergencies. Traditional Traffic Management Systems are based on the principles of static control combined with human response, which lack the necessary dynamic capabilities in order to respond to the real-time variation of traffic conditions. This often results in extended congestion, slower emergency response times, and inefficient traffic flow, contributing to increased pollutionand travel delays. Current technologies are however full of computer vision and AI approaches but are still in decline in their respective speeds, accuracies and flexibility especially within the urban context.
The smart urban traffic system with real-time vehicle tracking grasps advanced AI and computer vision techniques. The system utilizes YOLOv11 for high-accuracy vehicle detection, paired with BotSort to ensure consistent tracking even in complex traffic situations. A key feature of the invention is an emergency vehicle detection functionality, which allows it to detect and track critical vehicles and appropriately modify traffic signal conditions to ensure that they have clear paths for effective response.
The system also incorporates TransferFlow which is an effective data handling technique that enables processing of big volume traffic data without issues. Real-time optimal control policies for traffic management systems are also developed using Deep Q-learning, a variety of reinforcement learning by which congestion is controlled and avoided before it occurs. The aim of this combined approach is to develop a smarter, safer and more adaptable traffic management system for modern urban environments.
The innovation disclosed in US10395522B2, an adaptive traffic control, has its systems and methods to treat anomalies detected in traffic conditions using unmanned aerial vehicles(UAVs). One of these methods involves receiving traffic data at road junctions, determining statistical analyses of the data which makes traffic-related statistics and identifying traffic anomalies based upon the statistics. A UAV is then deployed to determine the detected anomaly. This anomaly root cause is sent to a light controller for processing traffic at the road junctions.
US9996798B2, it describes a computer system prepared for handling a traffic prediction request as related to systems and techniques for improving traffic prediction accuracy and comparisons of prediction models. It utilizes a historic data set, which is in turn selected based on the specific day and hour when the request was received, and that first or second model prediction error is chosen as the output for the traffic prediction. It is possible to obtain that model submitting a smaller prediction error, and thus have authorized the prediction of traffic flow most accurately possible.
US/2022/0020269A1, it describes that the control technique and system will communicate with all existing and new traffic control systems, thus extending the applications for real-time traffic information. This system can predict and detect the traffic congestion, as well as determine corrective actions using AI technologies. The corrective actions will be performed using the required data and instructions that will be transferred to the appropriate systems and mechanisms through well suited interfaces designed for this purpose.
US2008/0204277A1, it describes the Vehicle Infrastructure Integration(VII) aims to improve communication between vehicles and infrastructure for better safety and traffic management. The main objective is to use wireless data to predict vehicle directions and fine-tune the signal timing to improve safety in delay reduction by about 18-20% at road junctions and 5-10% over the whole street system in those areas. Signal detectors are no longer necessary, this will decrease the total cost of maintenance and improve traffic flow while VII converts adaptive control systems to real-time vehicle data processing for efficient traffic signal management and thus assist smooth driving.
US6989766B2, it describes the traffic signals that are broadcasted onto a road network through the signals and the vehicles traveling on roads controlled by these signals. Traffic lights continuously send their location, state, cycle changes, and timing data. A receiving system in the vehicle is intended to pick up this information and present it to the driver through visual and/or audible alert. By this information, drivers will be given an ideal speed range within which he or she should drive, hence reducing those stops and starts, effective highway usages.

Summary of the Invention
This advanced traffic management system revolutionizes urban traffic control by seamlessly combining deep learning, computer vision, and reinforcement learning. Using high-definition cameras, it analyzes traffic in real time. It identifies vehicles with YOLOv11, tracks them with BotSort, and uses GBML-DQN reinforcement learning to optimize traffic signals. The system's AI continuously monitors traffic, dynamically adjusts signal timings based on congestion levels, and improves overall traffic flow. By understanding and responding to traffic conditions automatically, the system enhances transportation efficiency and reduces congestion.
This system not only improves daily traffic flow, but it also gives priority to emergency vehicles by detecting ambulances, fire trucks, and police cars in real time. The system changes the traffic signals instantly when an emergency vehicle is spotted, creating a clear path for fast transit. By reducing delays at busy junctions, this technology improves response times for emergencies, potentially saving lives. Additionally, it reduces vehicle idling time, which saves fuel and helps the environment because there are fewer emissions. The system's purpose is to change how traffic is managed in cities by using AI-driven automation, which makes it safer on the roads and helps the environment.

Brief Description of Drawings
The innovation will be described in detail with the reference to the model embodiment shown in the figures wherein:
Figure-1: Architecture diagram for the proposed Smart Traffic Management System.
Figure-2: Flowgorithm representing the work flow of the Smart Traffic Management System.

Detailed Description of the Invention
The invention includes three main parts that work together to create a smart traffic management system. The first part, called Data Collection and Preprocessing, gathers live video from high-quality cameras placed at intersections. It improves the video by making it clearer, less noisy, and not distorted. By using advanced techniques like Gaussian blurring and contrast adjustments, it makes the picture quality better, which makes it easier to find and track vehicles more efficiently. This preprocessing step makes sure that the next steps, which are detection and tracking, work as well as possible.
Vehicle Detection and Tracking in Real-Time, utilizes the cutting-edge YOLOv11 deep learning model to accurately detect vehicles. This model distinguishes various vehicle types (cars, trucks, motorcycles, buses) for thorough road surveillance. Once detected, vehicles are continually tracked by BotSort, an advanced tracking algorithm that assigns specific identities to each vehicle across frames. This tracking enables monitoring vehicle movement in various lanes, identifying traffic offenses, and analyzing traffic congestion. Combining real-time detection and tracking, the system estimates traffic density, categorizes vehicle types, and extracts valuable data insights.
Third part of the traffic signal system employs artificial intelligence to optimize traffic signals in real time. It uses a trained machine learning model to analyze traffic conditions and set optimal signal timings, ensuring smoother traffic flow. Unlike traditional fixed-timing systems, this AI-based approach can adapt to changing traffic patterns. Before deployment, the model was trained on real-world traffic data in a simulated environment. The system continuously learns and self-optimizes, improving its accuracy in predicting optimal signal timings. This adaptive approach reduces congestion and enhances overall traffic movement efficiency.
The system has the ability to quickly detect emergency vehicles like ambulances, fire trucks, and police cars using advanced deep learning technology. When an emergency vehicle is detected, the system changes the traffic lights to make a clear path for it to get to its destination faster. This is a very important feature because it can help emergency vehicles get to where they need to go faster, which can save lives. The system can also give emergency vehicles a clear path without slowing down the rest of the traffic, making it a useful tool for cities.
The system includes measures to promote environmental and energy efficiency by minimizing vehicle idling at traffic lights. Conventional traffic systems frequently lead to excessive fuel usage because of long waits at red lights. By adjusting traffic signals dynamically based on real-time traffic conditions, the system reduces unnecessary stops. This not only reduces emissions but also fosters environmental sustainability. The AI-powered solution ensures efficient traffic flow, resulting in lower greenhouse gas emissions and improved air quality in urban environments.
The Smart Traffic Management System is a cutting-edge solution that uses artificial intelligence (AI) to transform urban traffic management. It uses real-time vehicle tracking, advanced traffic prediction, and signal optimization to improve traffic flow, minimize congestion, and prioritize emergency vehicles. It integrates with existing infrastructure to create an adaptable system that uses data to improve traffic management. The AI's ability to learn continuously makes this system scalable and ready for future advancements in transportation infrastructure, making cities safer, more efficient, and greener.

The figure 2, depicts the suggested architecture system of the Smart Urban Traffic System with Real-Time Vehicle Tracking comprises a number of major components that collaborate to streamline traffic and maximize urban mobility. The system starts with video data collection and preprocessing, where high-definition cameras placed at traffic intersections continuously record video. The raw video data is preprocessed through enhancement and frame preparation to ensure precision in subsequent analysis.
Vehicle detection is accomplished through YOLOv11, providing real-time vehicle identification, their classes, locations, and movements within the video frames being processed. Vehicle detection leads to the tracking of detected vehicles using BotSort, which allocates IDs to recognized vehicles. The approach guarantees precise journey tracking between several frames, enhancing resource access and traffic management performance.
Emergency vehicle detection is an important feature of the system, using specialized detectors to detect emergency vehicles based on distinctive signals. When an emergency vehicle is detected, the system dynamically controls traffic signals to give a clear route, allowing for smooth passage. When there are no emergency vehicles, traffic optimization operates normally, aimed at minimizing congestion and ensuring optimal traffic flow.
Traffic flow optimization is realized through Deep Q-Learning, a sophisticated reinforcement learning algorithm that utilizes real-time and past traffic data to calculate the ideal duration for traffic signals. Through dynamic adjustments of signal timings, the system strives to reduce congestion and improve the overall quality of traffic flow. The optimized signal timing and priority allowances for emergency vehicles are communicated to the Traffic Signal Controller, which makes the requisite adjustments in junctions.
All of the traffic information, optimization choices, and real-time monitoring data are maintained in a central database for long-term investigation. The stored data helps improve the system continuously by offering valuable information on traffic trends and patterns. A Traffic Insights Dashboard provides real-time and historical data visualization, allowing city planners to track traffic conditions in an effective manner and make decisions accordingly for future traffic management planning.

The Smart Traffic Management System uses real-time technology to improve traffic flow. It finds vehicles using YOLOv11 and tracks them using BotSort. This system knows how busy the traffic is, where there are jams, and if there are any problems. It also uses a type of AI called GBML-DQN to make decisions about when to change the traffic signals, which reduces traffic jams, speeds up traffic, and makes the roads better overall. One of the best things about this system is that it can tell the difference between emergency vehicles, like ambulances, fire trucks, and police cars, and give them the right of way, so they can get to where they need to go faster. This can save lives and make emergency situations better.This system can spot problems in traffic, like crashes and tie-ups, and take action before they get worse. This makes it easier to control traffic overall. It also uses data and automates things, so there's less need for people to keep an eye on traffic by hand. This means that city transportation systems can grow and change more easily. The system uses computer vision, deep learning, and real-time data analysis to change how traffic is managed. As a result, cities become safer, more efficient, and better for the environment.
Equivalents
The present invention leverages the functionalities of specific technologies, such as YOLOv11 for object detection, BotSort for vehicle tracking, PyTorch for data management, and Deep Q-learning for adaptive traffic control, the invention is not restricted to only these implementations. It can use equivalent object detection algorithms and tracking algorithms, reinforcement learning models, or data optimization frameworks with similar functionalities without deviating from the concept and spirit of the invention.It also includes other machine learning techniques including alternative reinforcement learning algorithms or data-handling systems that provide efficient real-time processing. It would cover all systems and methods that could achieve results similar to these for improving urban traffic flow, emergency response, and congestion management. , Claims:Claim:
1. A smart urban traffic system with real-time vehicle trackingcomprising,
a. A object detection module based on the YOLOv11 algorithm for recognizing vehicles in various city scenes, such as low-visibility, high-density traffic, and occlusion, supplying real-time information to adaptive traffic control.
b. A tracking module based on the BotSort algorithm for confirming vehicle identification and persistent tracking from frame to frame, supporting authentic vehicle counting even in dense traffic situations.
c. A emergency vehicle detection module to classify and prioritize emergency vehicles like ambulances and fire trucks by dynamically adjusting traffic signals to clear the way for their movement.
2. According to claim 1, the vehicle detection and tracking system provides real-time, reliable vehicle identification and tracking in dense urban areas,improving the accuracy of traffic management decisions.
3. As per claim 1, the traffic signal control based on reinforcement learning dynamically adjusts timings with Deep Q-learning, minimizing congestion by taking into account real-time traffic conditions as well as past congestion patterns, and giving priority to emergency vehicles for effective urban mobility.